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在整个幻灯片成像上使用多标签圆圈表示的eosinophils实例对象分割.

Yilin Liu1, Ruining Deng1, Juming Xiong1

  • 1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

Proceedings of SPIE--the International Society for Optical Engineering
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PubMed
概括
此摘要是机器生成的。

一个新的多标签CircleSnake模型增强了用于诊断eosinophilic食道炎 (EOE) 的eosinophil细分. 这种自动化方法提高了鉴定食道炎症的准确性和效率,有助于临床评估.

关键词:
环形蛇 环形蛇 环形蛇埃索诺菲尔性食道炎 (Eosinophilic Esophagitis) 是一种导致食道炎的疾病.面具R-CNN是指一个R-CNN的面具.

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科学领域:

  • 医学诊断 医学诊断 医学诊断
  • 计算病理学计算病理学
  • 图像分析 图像分析

背景情况:

  • 性食道炎 (EoE) 是食道的慢性炎症状况.
  • 诊断EoE依赖于计数食道的乙氨基酸 (Eos),这是一个劳动密集的手工过程.
  • 目前的方法缺乏效率,可能是主观的,需要自动化解决方案.

研究的目的:

  • 开发一种自动化方法,用于在EoE诊断中准确地细分埃索诺菲尔实例.
  • 为了适应CircleSnake模型用于多标签的好色素细胞细分.
  • 将拟议模型的性能与Mask R-CNN等现有方法进行比较.

主要方法:

  • 提出了一个多标签的CircleSnake模型,扩展了原来的单标签CircleSnake.
  • 应用该模型在食道组织图像中实例细分乙氨基细胞 (Eos).
  • 使用平均精度 (AP) 评估性能,并与Mask R-CNN和DeepSnake进行比较.

主要成果:

  • 多标签的CircleSnake模型在识别和细分乙酸细胞方面表现出卓越的性能.
  • 与Mask R-CNN和DeepSnake模型相比,实现了更高的平均精度 (AP).
  • 自动化方法简化了EoE的评估过程.

结论:

  • 多标签的CircleSnake模型提供了一个有前途的自动化解决方案,用于EoE中的乙酸细胞细分.
  • 这一进步可以提高临床实践中的诊断准确性和效率.
  • 公共可用的源代码有助于进一步的研究和应用.